Transcriptional regulation of protein complexes and biological pathways

Sequence variation in promoter of Ica1 gene, which encodes protein implicated in type 1 diabetes, causes transcription factor autoimmune regulator (AIRE) to increase its binding and down-regulate expression

ICA69 (islet cell autoantigen 69 kDa) is a protein implicated in type 1 diabetes mellitus in both the non-obese diabetic (NOD) mouse model and humans. ICA69 is encoded by the Ica1 gene on mouse chromosome 6 A1-A2. We previously reported reduced ICA69 expression in the thymus of NOD mice compared with thymus of several non-diabetic mouse strains. We propose that reduced thymic ICA69 expression could result from variations in transcriptional regulation of the gene and that polymorphisms within the Ica1 core promoter may partially determine this transcriptional variability. We characterized the functional promoter of Ica1 in NOD mice and compared it with the corresponding portions of Ica1 in non-diabetic C57BL/6 mice. Luciferase reporter constructs demonstrated that the NOD Ica1 promoter region exhibited markedly reduced luciferase expression in transiently transfected medullary thymus epithelial (mTEC(+)) and B-cell (M12)-derived cell lines. However, in a non-diabetic strain, C57BL/6, the Ica1 promoter region was transcriptionally active when transiently transfected into the same cell lines. We concomitantly identified five single nucleotide polymorphisms within the NOD Ica1 promoter. One of these single nucleotide polymorphisms increases the binding affinity for the transcription factor AIRE (autoimmune regulator), which is highly expressed in thymic epithelial cells, where it is known to play a key role regulating self-antigen expression. We conclude that polymorphisms within the NOD Ica1 core promoter may determine AIRE-mediated down-regulation of ICA69 expression in medullary thymic epithelial cells, thus providing a novel mechanistic explanation for the loss of immunologic tolerance to this self-antigen in autoimmunity.

Computational identification of transcriptional regulators in human endotoxemia

One of the great challenges in the post-genomic era is to decipher the underlying principles governing the dynamics of biological responses. As modulating gene expression levels is among the key regulatory responses of an organism to changes in its environment, identifying biologically relevant transcriptional regulators and their putative regulatory interactions with target genes is an essential step towards studying the complex dynamics of transcriptional regulation. We present an analysis that integrates various computational and biological aspects to explore the transcriptional regulation of systemic inflammatory responses through a human endotoxemia model. Given a high-dimensional transcriptional profiling dataset from human blood leukocytes, an elementary set of temporal dynamic responses which capture the essence of a pro-inflammatory phase, a counter-regulatory response and a dysregulation in leukocyte bioenergetics has been extracted. Upon identification of these expression patterns, fourteen inflammation-specific gene batteries that represent groups of hypothetically ‘coregulated’ genes are proposed. Subsequently, statistically significant cis-regulatory modules (CRMs) are identified and decomposed into a list of critical transcription factors (34) that are validated largely on primary literature. Finally, our analysis further allows for the construction of a dynamic representation of the temporal transcriptional regulatory program across the host, deciphering possible combinatorial interactions among factors under which they might be active. Although much remains to be explored, this study has computationally identified key transcription factors and proposed a putative time-dependent transcriptional regulatory program associated with critical transcriptional inflammatory responses. These results provide a solid foundation for future investigations to elucidate the underlying transcriptional regulatory mechanisms under the host inflammatory response. Also, the assumption that coexpressed genes that are functionally relevant are more likely to share some common transcriptional regulatory mechanism seems to be promising, making the proposed framework become essential in unravelling context-specific transcriptional regulatory interactions underlying diverse mammalian biological processes.

Transcriptional regulation via TF-modifying enzymes: an integrative model-based analysis

Transcription factor activity is largely regulated through post-translational modification. Here, we report the first integrative model of transcription that includes both interactions between transcription factors and promoters, and between transcription factors and modifying enzymes. Simulations indicate that our method is robust against noise. We validated our tool on a well-studied stress response network in yeast and on a STAT1-mediated regulatory network in human B cells. Our work represents a significant step toward a comprehensive model of gene transcription.

Motifs and cis-regulatory modules mediating the expression of genes co-expressed in presynaptic neurons

An integrative strategy of comparative genomics, experimental and computational approaches reveals aspects of a regulatory network controlling neuronal-specific expression in presynaptic neurons.

Background

Hundreds of proteins modulate neurotransmitter release and synaptic plasticity during neuronal development and in response to synaptic activity. The expression of genes in the pre- and post-synaptic neurons is under stringent spatio-temporal control, but the mechanism underlying the neuronal expression of these genes remains largely unknown.

Results

Using unbiased in vivo and in vitro screens, we characterized the cis elements regulating the Rab3A gene, which is expressed abundantly in presynaptic neurons. A set of identified regulatory elements of the Rab3A gene corresponded to the defined Rab3A multi-species conserved elements. In order to identify clusters of enriched transcription factor binding sites, for example, cis-regulatory modules, we analyzed intergenic multi-species conserved elements in the vicinity of nine presynaptic genes, including Rab3A, that are highly and specifically expressed in brain regions. Sixteen transcription factor binding motifs were over-represented in these multi-species conserved elements. Based on a combined occurrence for these enriched motifs, multi-species conserved elements in the vicinity of 107 previously identified presynaptic genes were scored and ranked. We then experimentally validated the scoring strategy by showing that 12 of 16 (75%) high-scoring multi-species conserved elements functioned as neuronal enhancers in a cell-based assay.

Conclusions

This work introduces an integrative strategy of comparative genomics, experimental, and computational approaches to reveal aspects of a regulatory network controlling neuronal-specific expression of genes in presynaptic neurons.

Integrative analysis of cancer pathway progression and coherence

We analyzed the cancer pathways in the KEGG (Kyoto Encyclopedia of Genes and Genomes) database. The database provides a collective of signaling pathway members involved in cancer progression. However, the KEGG cancer pathways, unlike signaling pathways, have not been analyzed extensively with gene expression and mutation data. We transformed the colorectal cancer pathway into discrete X and Y scales and analyzed the relative expression levels of adenoma and carcinoma samples as well as the distribution of mutation targets. The X scale corresponds to the downstream location in a pathway, whereas the Y scale corresponds to the stage of the tumor. The gene expression values of the early stage pathway members are significantly higher than of the rest of the pathway members in colorectal adenoma tissues. The colorectal cancer pathway shows some degree of coherence in the carcinoma samples. The correlated gene pairs responsible for the coherence of the colorectal cancer pathway in the carcinoma samples are supported, in part, by the literature and may suggest novel regulatory associations. Finally, there are more mutation targets in the nucleus as well as the late tumor stages of the KEGG colorectal cancer pathway.

Eukaryotic transcription factor binding sites--modeling and integrative search methods

A comprehensive knowledge of transcription factor binding sites (TFBS) is important for a mechanistic understanding of transcriptional regulation as well as for inferring gene regulatory networks. Because the DNA motif recognized by a transcription factor is typically short and degenerate, computational approaches for identifying binding sites based only on the sequence motif inevitably suffer from high error rates. Current state-of-the-art techniques for improving computational identification of binding sites can be broadly categorized into two classes: (1) approaches that aim to improve binding motif models by extracting maximal sequence information from experimentally determined binding sites and (2) approaches that supplement binding motif models with additional genomic or other attributes (such as evolutionary conservation). In this review we will discuss recent attempts to improve computational identification of TFBS through these two types of approaches and conclude with thoughts on future development.

TREMOR--a tool for retrieving transcriptional modules by incorporating motif covariance

A transcriptional module (TM) is a collection of transcription factors (TF) that as a group, co-regulate multiple, functionally related genes. The task of identifying TMs poses an important biological challenge. Since TFs belong to evolutionarily and structurally related families, TF family members often bind to similar DNA motifs and can confound sequence-based approaches to TM identification. A previous approach to TM detection addresses this issue by pre-selecting a single representative from each TF family. One problem with this approach is that closely related transcription factors can still target sufficiently distinct genes in a biologically meaningful way, and thus, pre-selecting a single family representative may in principle miss certain TMs. Here we report a method—TREMOR (Transcriptional Regulatory Module Retriever). This method uses the Mahalanobis distance to assess the validity of a TM and automatically incorporates the inter-TF binding similarity without resorting to pre-selecting family representatives. The application of TREMOR on human muscle-specific, liver-specific and cell-cycle-related genes reveals TFs and TMs that were validated from literature and also reveals additional related genes.

Position and distance specificity are important determinants of cis-regulatory motifs in addition to evolutionary conservation

Computational discovery of cis-regulatory elements remains challenging. To cope with the high false positives, evolutionary conservation is routinely used. However, conservation is only one of the attributes of cis-regulatory elements and is neither necessary nor sufficient. Here, we assess two additional attributes—positional and inter-motif distance specificity—that are critical for interactions between transcription factors. We first show that for a greater than expected fraction of known motifs, the genes that contain the motifs in their promoters in a position-specific or distance-specific manner are related, both in function and/or in expression pattern. We then use the position and distance specificity to discover novel motifs. Our work highlights the importance of distance and position specificity, in addition to the evolutionary conservation, in discovering cis-regulatory motifs.

Integrating Synapse Proteomics with Transcriptional Regulation

The mammalian postsynaptic proteome (PSP) comprises a highly interconnected set of approximately 1,000 proteins. The PSP is organized into macromolecular complexes that have a modular architecture defined by protein interactions and function. Signals initiated by neurotransmitter receptors are integrated by these complexes and their constituent enzymes to orchestrate multiple downstream cellular changes, including transcriptional regulation of genes at the nucleus. Genome wide transcriptome studies are beginning to map the sets of genes regulated by the synapse proteome. Conversely, understanding the transcriptional regulation of genes encoding the synapse proteome will shed light on synapse formation. Mutations that disrupt synapse signalling complexes result in cognitive impairments in mice and humans, and recent evidence indicates that these mutation change gene expression profiles. We discuss the need for global approaches combining genetics, transcriptomics and proteomics in order to understand cognitive function and disruption in diseases.

Analysis of promoter regions of co-expressed genes identified by microarray analysis

Background

The use of global gene expression profiling to identify sets of genes with similar expression patterns is rapidly becoming a widespread approach for understanding biological processes. A logical and systematic approach to study co-expressed genes is to analyze their promoter sequences to identify transcription factors that may be involved in establishing specific profiles and that may be experimentally investigated.

Results

We introduce promoter clustering i.e. grouping of promoters with respect to their high scoring motif content, and show that this approach greatly enhances the identification of common and significant transcription factor binding sites (TFBS) in co-expressed genes. We apply this method to two different dataset, one consisting of micro array data from 108 leukemias (AMLs) and a second from a time series experiment, and show that biologically relevant promoter patterns may be obtained using phylogenetic foot-printing methodology. In addition, we also found that 15% of the analyzed promoter regions contained transcription factors start sites for additional genes transcribed in the opposite direction.

Conclusion

Promoter clustering based on global promoter features greatly improve the identification of shared TFBS in co-expressed genes. We believe that the outlined approach may be a useful first step to identify transcription factors that contribute to specific features of gene expression profiles.

Identification and cross-species comparison of canine osteoarthritic gene regulatory cis-elements

OBJECTIVE

To better understand transcription regulation of osteoarthritis (OA) by examining common promoter motifs in canine osteoarthritic genes, to identify other genes containing these motifs and to assess the conservation of these motifs between canine, human, mouse and rat.

DESIGN

Differentially expressed transcripts in canine OA were mapped to the human genome. We thus identified 20 orthologous human transcripts representing 19 up-regulated genes and 62 orthologous transcripts representing 60 down-regulated genes. The 5 kbp upstream regions of these transcripts were used to identify binding sites and build promoter models based on those sites. The human genome was subsequently searched for other transcripts likely to be regulated by the same promoter models. Orthologous transcripts were then identified in canine, rat and mouse for determination of potential cross-species conservation of binding sites comprising the promoter model.

RESULTS

Four promoter models containing 5-6 transcripts and 5-8 common transcription factor binding sites were developed. They include binding sites for AP-4, AP-2alpha and gamma, and E2F. Several hundred other human genes were found to contain these promoter motifs. Furthermore these motifs were significantly over represented in the orthologous genes in canine, rat and mouse genomes.

CONCLUSIONS

We have developed and applied a computational methodology to identify common promoter elements implicated in OA and shared amongst four higher vertebrates. The transcription factors associated with these binding sites and other genes driven by these promoter motifs have been implicated in OA, chondrocyte development and with other biological factors involved in the disease.

Differential transcriptional response to nonassociative and associative components of classical fear conditioning in the amygdala and hippocampus

Classical fear conditioning requires the recognition of conditioned stimuli (CS) and the association of the CS with an aversive stimulus. We used Affymetrix oligonucleotide microarrays to characterize changes in gene expression compared to naive mice in both the amygdala and the hippocampus 30 min after classical fear conditioning and 30 min after exposure to the CS in the absence of an aversive stimulus. We found that in the hippocampus, levels of gene regulation induced by classical fear conditioning were not significantly greater than those induced by CS alone, whereas in the amygdala, classical fear conditioning did induce significantly greater levels of gene regulation compared to the CS. Computational studies suggest that transcriptional changes in the hippocampus and amygdala are mediated by large and overlapping but distinct combinations of molecular events. Our results demonstrate that an increase in gene regulation in the amygdala was partially correlated to associative learning and partially correlated to nonassociative components of the task, while gene regulation in the hippocampus was correlated to nonassociative components of classical fear conditioning, including configural learning.

Comprehensive analysis of pathway or functionally related gene expression in the National Cancer Institute's anticancer screen

We have analyzed the level of gene coregulation, using gene expression patterns measured across the National Cancer Institute's 60 tumor cell panels (NCI(60)), in the context of predefined pathways or functional categories annotated by KEGG (Kyoto Encyclopedia of Genes and Genomes), BioCarta, and GO (Gene Ontology). Statistical methods were used to evaluate the level of gene expression coherence (coordinated expression) by comparing intra- and interpathway gene-gene correlations. Our results show that gene expression in pathways, or groups of functionally related genes, has a significantly higher level of coherence than that of a randomly selected set of genes. Transcriptional-level gene regulation appears to be on a "need to be" basis, such that pathways comprising genes encoding closely interacting proteins and pathways responsible for vital cellular processes or processes that are related to growth or proliferation, specifically in cancer cells, such as those engaged in genetic information processing, cell cycle, energy metabolism, and nucleotide metabolism, tend to be more modular (lower degree of gene sharing) and to have genes significantly more coherently expressed than most signaling and regular metabolic pathways. Hierarchical clustering of pathways based on their differential gene expression in the NCI(60) further revealed interesting interpathway communications or interactions indicative of a higher level of pathway regulation. The knowledge of the nature of gene expression regulation and biological pathways can be applied to understanding the mechanism by which small drug molecules interfere with biological systems.